Zypher Network has formed a new collaboration between the decentralized AI agent market and computing infrastructure provider Nebulai. This collaboration includes providing a verifiable, privacy-reserved environment for AI agents development and deployment to incorporate Zypher Trust Technologies into the Nebulai platform.
🤝Zifer Network x Neblay!
We are pleased to announce a new partnership with the decentralized AI agent market and open computing network @NebulaihQ.
– Zypher Network (Employment) (@zypher_network) July 22, 2025
Nebulai offers OpenCompute Permissionless Compute Infrastructure. This web-based platform grants crowdsourced access and provides computing power for privacy-sensitive calculations such as AI algorithms, image rendering, special hardware and unconfigured zero-knowledge proof (ZK) and multi-party calculations (MPC).
This network enables transparent, auditable performance of AI processes based on Zypher’s Zero-knowledge Trust Technologies integration.
Zypher Network proposes trust-based AI agent coordination
Zypher core technology, created with decentralized AI application ideas, enables Neblai users and developers to verify the actions and effectiveness of AI agents. Prompt proof allows AI responses to be linked to initial input, and ZKTL provides an encrypted proof of data integrity across agents and external information exchanges.
Collaboration provides urgent requirements for verifiability in distributed AI processes. Integrated solutions can provide real scenario assurances as they are sealed in interactions, how agents coordinate with each other. Additionally, by lowering the barriers to trust between contributors, it can make it easier to contribute to AI.
Developer Access and Expanding Practical Use Cases
This partnership adds value by helping to the rise of AI solutions that combine Neblai with Zypher to provide privacy. Both organizations have open calculations and verifiable executive capabilities. AI agents can now be deployed in a secure, distributed environment for developers with unique transparency.
This collaboration further expands the scope of executable AI applications that run in unreliable settings, such as autonomous adjustments and privacy-sensitive calculations. The result is a broader application of AI in highly tuned and sensitive data fields.
